By minimizing the bounds obtained in Section , we can
develop PAC-Bayes bound-minimization algorithms for
meta-learning with deep neural networks. Here, for (REF ), and in view of [1]} we obtain a meta-learning algorithm. Similar algorithms can be found for
all bounds presented in Section .
Following previous
work [1]}, we consider \(\mathcal {W}=\lbrace h_w,w\in \mathbb {R}^d\rbrace \) 
as a set of neural networks with certain parameters. For \(\mathrm {N}_p, d\in \mathbb {N}\) , let \(\mathcal {U}\subset \mathbb {R}^{\mathrm {N}_p}\)  and \(\mathcal {W}\subset \mathbb {R}^d\)  for all tasks \(t_i\) . Like previous works, [1]}, [4]},
we
set the hyper-prior distribution as
\(\mathcal {P}\triangleq \mathcal {N}(0,\kappa _p^2\mathrm {I}_{\mathrm {N}_p}\times \mathrm {I}_{\mathrm {N}_p}), \) 
